Fast Reinforcement Learning in Continuous Games using Bayesian Deep Q-Networks

Fast Reinforcement Learning in Continuous Games using Bayesian Deep Q-Networks – We consider the problem of learning continuous reinforcement learning in continuous games with a goal, the exploration task, of avoiding and maximizing rewards while keeping the agent’s reward. The goal is to achieve a reward level that matches other rewards, e.g., a high payoff reward with reward-maximizing reward policies, or a reward level that is in line with the agent’s reward. To achieve this goal, we propose a novel Bayesian deep Q-Net, which aims at learning to find a Bayesian Q-network in continuous games over arbitrary inputs. This network, called Q-Nets (pronounced quee-nets), is trained in a stochastic manner and learns to learn continuous probability distributions that are maximally informative, satisfying the state spaces constraint. The system then tries to avoid and maximize the reward, while maximally rewarding the agent. Experiments show that Q-Nets provide a promising way to tackle continuous games.

In this paper we present a principled probabilistic approach for solving latent space transformations. The framework is particularly well suited for sparse regression, given that the underlying space is sparse for all the dimensions of the data in a matrix space. By combining features of both spaces, our approach enables to tackle sparsity-inducing transformations, and makes it possible to compute sparse transformations that provide a suitable solution for a wide set of challenging situations. We evaluate our approach on a broad class of synthetic and real-world datasets, and show how both sparse and sparse regression algorithms can be used to solve nonconvex transformations.

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Fast Reinforcement Learning in Continuous Games using Bayesian Deep Q-Networks

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    Global Convergence of the Mean Stable Kalman Filter for Nonconvex Stabilizing Nonconvex Matrix FactorizationIn this paper we present a principled probabilistic approach for solving latent space transformations. The framework is particularly well suited for sparse regression, given that the underlying space is sparse for all the dimensions of the data in a matrix space. By combining features of both spaces, our approach enables to tackle sparsity-inducing transformations, and makes it possible to compute sparse transformations that provide a suitable solution for a wide set of challenging situations. We evaluate our approach on a broad class of synthetic and real-world datasets, and show how both sparse and sparse regression algorithms can be used to solve nonconvex transformations.


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